Papers › Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
Ji Young Lee, Franck Dernoncourt
Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Dialogue Act Classification | Switchboard corpus | CNN[[Lee and Dernoncourt2016]] | Accuracy | 73.1 | #11 of 11 | Archive leaderboard | report |
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